Multispectral analysis of vegetation for remote sensing applications
Keywords:
Vegetation index, unmanned aerial vehicle uav, remote sensing, free software.
Abstract
This paper describes the development of a hardware and software tool for analysis of vegetation cover using remote sensing techniques. Begins with a study of the interaction of electromagnetic waves in the leaves of plants on the single and multiple plate models for the spectrum of visible and infrared, which allows using an application developed in Python found the spectral signature of coverage according to parameters of the leaves (known as an inversion model), which accounts for an approximation of what may occur with the reflectance of the surface of the leaves. The development of a system for acquiring and processing images in the visible and near infrared bands for use in precision agriculture through an unmanned aerial vehicle UAV is also explained.
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References
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[15] G. Suresh, G. Heygster, G. Bohrmann, C Melsheimer and J. H. Korber, “An automatic detection system for natural oil seep origin estimation in SAR images,” Proc. in Int. Geoscience and Remote Sensing Symposium, IGARSS´13, vol., no., pp.3566-3569, July 21- 26, 2013.
[16] J. D. Beezley, M. Martin, P. Rosen, J. Mandel and A. K. Kochanski, “Data management and analysis with WRF and SFIRE,” Proc. in Int. Geoscience and Remote Sensing Symposium, IGARSS´12, vol., no., pp. 5274-5277, July 22-27, 2012.
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[19] M. D. Iordache, L. Tits, J. M. Bioucas, A. Plaza and B. Somers, “A dynamic unmixing framework for plant production system monitoring,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.7, no.6, pp. 2016-2034, Jun. 2014.
[20] M. Zortea and A. Plaza, “Spatial-spectral endmember extraction from remotely sensed hyperspectral images using the watershed transformation,” presented at IEEE International Geoscience and Remote Sensing Symposium, IGARSS´10, vol. 1, pp. 963-966, July 25-30, 2010.
[2] A. Srinivasan, “Site-specific management for selected cropping systems in Asia: Progress and Prospects,” presented at the Int. Conf. on Agricultural Science and Technology ICAST, ´01, Beijing, China, November 7-9, 2001.
[3] A. Jiménez, D. Ravelo and J. Gómez, “Sistema de adquisición, almacenamiento y análisis de información fenológica para el manejo de plagas y enfermedades de un duraznero mediante tecnologías de agricultura de precisión,” Rev. Tecnura, vol. 14, no. 27, pp. 41-51, Dic. 2010.
[4] A. Abril and E. Bucher, “Overgrazing and soil carbon dynamics in the western Chaco of Argentina,” Applied Soil Ecology Journal, vol. 16, no. 3, pp. 243-243, 2001.
[5] A. Hoffer, “Remote Sensing: The Quantitative Approach,” in Biological and Physical considerations in applying Computer-Aided Analysis Techniques to Remote Sensor Data, Chapter 5, New York, US: McGraw-Hill, 1976, pp. 227-289.
[6] Y. Ching-Kuo, “Self-calibrated direct geo-referencing of airborne pushbroom hyperspectral images,” presented at IEEE Int. Geoscience and Remote Sensing Symposium IGARSS´11, Vancouver, Canada, July 24-29, 2011.
[7] Y. Zhang and X. Shen, “Approximate correction of length distortion for direct georeferencing in map projection frame,” IEEE Geoscience and Remote Sensing Letters, vol.10, no.6, pp.1419-1423, Nov. 2013.
[8] P. K., Varshney, Advanced image processing techniques for remotely sensed hyperspectral data. Berlin, Germany: Springer, 2004.
[9] F. V. Der Meer and S. M. De Jong, Imaging spectrometry: Basic principles and prospective applications. Dordrecht, Nederland: Springer, 2006.
[10] Z. Li, J. Chen and E. Baltsavias, Advances in photogrammetry, remote sensing and spatial information sciences. Boca Raton, U.S.: CRC Press, 2008.
[11] G. A. Carter, “Primary and secondary effects of water content on the spectral reflectance of leaves,” American Journal of Botany, vol. 78, no. 7, pp. 916-921, 1991.
[12] C. A. Wessman, and W.D. Bowman, “Estimation of biochemical constituents from fresh, green leaves by spectrum matching techniques,” Proc. In Int. Geoscience and Remote Sensing Symposium, IGARSS ‘90, vol. 2, pp. 971-974, College Park, MD, 1990.
[13] W. H. Hayt and J. A. Buck, Engineering electromagnetics. McGraw-Hill, 2012.
[14] G. Walter, F. Warmerdam and P. Farris-Manning, “An open source tool for geospatial image exploitation,” Proc. in Int. Geoscience and Remote Sensing Symposium, IGARSS ‘02, vol. 6, no. 1, pp. 3522-3524, Jun. 2002.
[15] G. Suresh, G. Heygster, G. Bohrmann, C Melsheimer and J. H. Korber, “An automatic detection system for natural oil seep origin estimation in SAR images,” Proc. in Int. Geoscience and Remote Sensing Symposium, IGARSS´13, vol., no., pp.3566-3569, July 21- 26, 2013.
[16] J. D. Beezley, M. Martin, P. Rosen, J. Mandel and A. K. Kochanski, “Data management and analysis with WRF and SFIRE,” Proc. in Int. Geoscience and Remote Sensing Symposium, IGARSS´12, vol., no., pp. 5274-5277, July 22-27, 2012.
[17] M. Miyamoto, K. Yoshino and K. Kushida, “Vegetation mapping of Kushiro wetland in northeast Hokkaido, Japan: application of SPOT images, aerial balloon photographs and airborne color near infrared (CNIR) images for classification,” Proc. in Int. Geoscience and Remote Sensing Symposium, IGARSS ‘02, vol.6, pp. 3495-3497, Jun. 24-28, 2002.
[18] H. Sun, M. Li, L. Zheng, Y. Zhang and W. Yang, “Evaluation of maize growth by ground based multi-spectral image,” presented at IEEE/SICE International Symposium on System Integration, SII´11, vol. 1, pp. 207-211, Dec. 20-22, 2011.
[19] M. D. Iordache, L. Tits, J. M. Bioucas, A. Plaza and B. Somers, “A dynamic unmixing framework for plant production system monitoring,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.7, no.6, pp. 2016-2034, Jun. 2014.
[20] M. Zortea and A. Plaza, “Spatial-spectral endmember extraction from remotely sensed hyperspectral images using the watershed transformation,” presented at IEEE International Geoscience and Remote Sensing Symposium, IGARSS´10, vol. 1, pp. 963-966, July 25-30, 2010.
Published
2015-11-06
How to Cite
Jiménez-López, A., Jiménez-López, M., & Jiménez-López, F. (2015). Multispectral analysis of vegetation for remote sensing applications. ITECKNE, 12(2), 156-167. https://doi.org/https://doi.org/10.15332/iteckne.v12i2.1242
Issue
Section
Research and Innovation Articles
